Medicare spending per enrollee and Clear days

Where Medicare spending per enrollee is higher, Clear days tends to be higher. The link is weak: +0.37 across 3,121 counties, on a scale from −1 (opposite) to +1 (in step). That figure compares places with the same population density, income and education, so those differences can't explain it. It holds inside states too. Measured separately within each of 40 states, the middle value of the link is +0.14.

Holds after adjusting

Every place, plotted

One dot for each place reporting both measures. Hover over or tap a dot to see its name and values. The thin line is the straight line that best fits the dots. The bold line, shown for counties, is the link after adjusting for density, income and education. A dot far from the lines is a place that does not follow the pattern.

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What each measure is

Medicare spending per enrollee

What Medicare spent per person in a year, for people in traditional Medicare, where Medicare pays each doctor and hospital bill itself. Medicare pays more for the same care where wages and rents are high. This figure takes those price gaps out, so it shows how much care people used, not what it cost locally. It is not adjusted for how sick people are. It leaves out people in private Medicare Advantage plans.

CMS Medicare Geographic Variation Public Use File · CMS Medicare Geographic Variation PUF, national/state/county, all ages, TOT_MDCR_STDZD_PYMT_PC

Clear days

The number of days in the year when clouds blocked less than 7% of the sunlight, so the ground got at least 93% of what a cloudless sky would give. It is the closest measure here to a count of sunny days. It is a NASA estimate from weather satellites for squares of land about 60 miles across, not a count by someone watching the sky, and nearby counties often share a value. The 93% line was chosen so that, on average, the count matches NOAA's counts of clear days at 64 weather stations. There, a clear day is one with three tenths of the sky or less covered by cloud in daylight. It starts in 2001, since the older satellites count clear days differently.

NASA POWER (Prediction Of Worldwide Energy Resources) · NASA POWER daily regional API, community RE, ALLSKY_SFC_SW_DWN / CLRSKY_SFC_SW_DWN

Highest and lowest

Where each measure is highest and lowest among the places reporting both (2023), with the other measure beside it. This is shown for every size of place where both are published. Metro-area values are added up from their counties. A measure that cannot be added up across counties, such as a middle value (median), has no metro figure. Counts are shown per 1,000 residents, since raw totals would only list the largest places. Cities and towns and ZIP codes are shown where the Census survey or the CDC publishes them; most other sources stop at the county. They are shown for places of 5,000 residents or more, and only where the survey’s margin of error is small enough for the figure to be usable. The CDC publishes its health estimates for cities in a version adjusted for age, so places with older or younger people can be compared fairly, but not for ZIP codes.

Counties

By Medicare spending per enrollee

	Medicare spending per enrollee	Clear days
Highest
Miller County, GA	27,129	124
Hamilton County, KS	24,463	175
Greer County, OK	22,580	181
Oglala Lakota County, SD	21,799	132
Loving County, TX	21,063	210
Lowest
Copper River Census Area, AK	5,333	26
Judith Basin County, MT	5,754	93
Denali Borough, AK	5,917	21
Yakutat City and Borough, AK	6,205	3
Arthur County, NE	6,331	147

By clear days

	Medicare spending per enrollee	Clear days
Highest
Yuma County, AZ	11,355	273
La Paz County, AZ	11,281	272
Imperial County, CA	14,150	258
Maricopa County, AZ	12,758	254
Gila County, AZ	11,771	236
Lowest
Yakutat City and Borough, AK	6,205	3
Juneau City and Borough, AK	9,426	3
Hoonah-Angoon Census Area, AK	10,412	3
Aleutians West Census Area, AK	8,126	5
Aleutians East Borough, AK	7,913	6

States

By Medicare spending per enrollee

	Medicare spending per enrollee	Clear days
Highest
Louisiana	13,805	119
Oklahoma	13,690	158
Florida	13,686	101
Mississippi	13,474	128
Texas	13,288	145
Lowest
Hawaii	8,203	80
Oregon	9,019	103
Alaska	9,082	17
Montana	9,198	82
Vermont	9,315	62

By clear days

	Medicare spending per enrollee	Clear days
Highest
Arizona	11,790	241
Nevada	12,325	186
New Mexico	9,793	168
California	12,125	168
Oklahoma	13,690	158
Lowest
Alaska	9,082	17
Vermont	9,315	62
Michigan	11,780	79
Hawaii	8,203	80
Maine	9,841	81

Metro areas

By Medicare spending per enrollee

	Medicare spending per enrollee	Clear days
Highest
Bogalusa, LA	17,954	107
Rio Grande City-Roma, TX	17,395	158
Vernon, TX	17,093	181
Alice, TX	16,760	157
Durant, OK	16,536	151
Lowest
Fairbanks-College, AK	7,065	29
Hilo-Kailua, HI	7,474	14
Kapaa, HI	7,492	101
Sheridan, WY	7,495	114
Kahului-Wailuku, HI	7,870	104

By clear days

	Medicare spending per enrollee	Clear days
Highest
Yuma, AZ	11,355	273
El Centro, CA	14,150	258
Phoenix-Mesa-Chandler, AZ	12,700	252
Payson, AZ	11,771	236
Lake Havasu City-Kingman, AZ	10,287	234
Lowest
Juneau, AK	9,426	3
Anchorage, AK	9,055	11
Hilo-Kailua, HI	7,474	14
Fairbanks-College, AK	7,065	29
Ketchikan, AK	9,887	33

Counties furthest from the pattern

Compared with the straight line that best fits all 3,143 counties: the five with far more clear days than their Medicare spending per enrollee would suggest, and the five with far less.

	Medicare spending per enrollee	Clear days
Above the line
Yuma County, AZ	11,355	273
La Paz County, AZ	11,281	272
Maricopa County, AZ	12,758	254
Imperial County, CA	14,150	258
Mohave County, AZ	10,287	234
Below the line
North Slope Borough, AK	19,064	46
Dillingham Census Area, AK	14,747	29
Hoonah-Angoon Census Area, AK	10,412	3
Haines Borough, AK	11,058	9
Sitka City and Borough, AK	14,079	28

Two series, one county

Both measures over time for a county you pick. The two charts are stacked, so they share only the years along the bottom. You can pick from the thirty largest counties reporting both. This panel covers 2010–2024, and a measure published only once shows as a single dot.

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Every version of the link

Each row measures the same link, from −1 to +1, in a different way. If the rows mostly agree, the link does not depend on one choice of method.

Before adjusting for anything	+0.354	3,121 counties reporting both
After adjusting for density, income and education	+0.371	the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size	+0.376	size taken out as well as density
Also comparing only within each state	+0.151	each county is compared only with others in its own state, so this is no longer a comparison between states
Also holding winter sunshine equal	—	compares counties that get the same sunlight in winter as well. If this is much weaker, part of the link is a difference between sunny and grey regions (more)
Giving bigger counties more weight	+0.256	the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 287 equally weighted counties
Middle value inside single states	+0.135	the link worked out inside each of 40 states with at least 20 counties reporting
Using ranks, after adjusting	+0.412	uses each county’s rank, not its value, so a few extreme counties cannot create the link

How much to believe it

Could it be chance?

The p-value is the chance of seeing a link this strong if there were really none. A value of 0.05 or less means the link is unlikely to be chance. Very small values are written in short form: 3.1e-16 means 0.00000000000000031, and the bigger the number after “e-”, the smaller the value. Treating every county as a separate case gives p = 1.5e-102. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 7.2e-6. The site tested 32,131 pairs, and testing so many turns up some flukes. The q-value allows for that too. For this pair it is 1.5e-101.

Do nearby places look alike?

Moran’s I measures how much nearby places look alike: 0 means no map pattern, and higher means neighbours are more similar. For the part of each county that the adjustment leaves unexplained, it is +0.81 (p = 0.001). That unexplained part still clusters on the map. So some regional factor shared by both measures is not accounted for.

Did they change together?

The figures above compare counties at one moment. This asks a different question: where Medicare spending per enrollee rose most, did clear daysmove too? Each county’s change is compared with the average change in its own state. That takes out ups and downs that hit a whole region at once. Each change is the gap between two estimates that both contain some error. That error pulls these links towards zero, so a weak one is weak partly for that reason. The q column shows whether a link could be a fluke, allowing for the many pairs tested: 0.05 or less means it is unlikely to be chance.

Period	Counties	Change together	Within states	q
2014–2019 the main test: each Census survey figure averages five years, and these two do not overlap; before the pandemic	3,141	-0.08	-0.03	0.616
2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years	3,141	+0.03	-0.02	0.617

Over the main period, any link between the changes could easily be chance. How change is measured.

Related pairs

Summer daily high and Clear days — +0.77
Days of 90°F or hotter and Clear days — +0.64
Average temperature and Clear days — +0.59
Cooling degree days and Clear days — +0.58
Wet days and Clear days — -0.57
Heating degree days and Clear days — -0.56
Medicare spending per enrollee and Cooling degree days — +0.54
Medicare spending per enrollee and Days of 90°F or hotter — +0.53

Association only. Nothing here shows that either measure changes the other. This describes counties, not the people in them. Two measures going together across places does not mean the same people have both. How the US county data works · Explore Medicare spending per enrollee and Clear days